Job Training, Remote Working, and Self-Employment: Displaced Workers Beyond Employment Hysteresis
Bibliographic record
Abstract
The recent SARS-Cov-2 pandemic has contributed to several corporate crises. As a result, many Small- and Medium-Sized Enterprises (SMEs) in Italy have filed for bankruptcy in the first quarter of 2020. In addition to a gigantic macroeconomic effect, the lockdown has impacted individuals to a large ex- tent. In this article, we investigate the behavioural response of employees who are under a dual condition of stress; namely, the pandemic and the risk of job loss. The hypothesis of employment hysteresis is challenged by looking at the tendency of individuals who are employed in firms facing a crisis, or in diffi- culty, to participate in training measures for: a similar job, remote working, and self-employment. Findings from a seemingly unrelated regressions (SUR) model show a significant increase in the likelihood to participate in standard or high-commitment training measures for similar jobs and remote working for employees who: i) positively value their professional social capital, i.e. their membership in a trade union (+24.4 and +25.2 percentage points, respectively); ii) have some displaced colleagues (+29.6 and +40.7 percentage points, respec- tively). Finally, we find that employees with a lower educational background are less likely to consider the possibility of switching between occupations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".